Enterprise Automation Architecture: Best Practices

Building automation workflows for a small team is one thing. Designing a robust, scalable automation architecture that serves an entire enterprise is another challenge altogether. This guide covers battle-tested patterns and practices for enterprise automation systems that handle millions of executions, serve thousands of users, and maintain 99.9%+ uptime.

Enterprise Automation Challenges

Before diving into solutions, let's understand the unique challenges enterprises face:

Scale and Performance

• Volume: Handling 10M+ workflow executions per month
• Concurrency: Supporting 1000+ concurrent workflows
• Throughput: Processing 100K+ events per hour
• Latency: Sub-second response times for critical workflows

Security and Compliance

• Data governance: SOC 2, GDPR, HIPAA compliance
• Access control: Role-based and attribute-based permissions
• Audit trails: Complete execution history and change tracking
• Secrets management: Secure credential storage and rotation

Reliability and Operations

• High availability: 99.9%+ uptime SLA
• Disaster recovery: RPO < 1 hour, RTO < 4 hours
• Monitoring: Real-time alerts and observability
• Support: 24/7 operations team coverage

Integration Complexity

• Legacy systems: Mainframes, on-premise databases, SOAP APIs
• Modern SaaS: 100+ cloud applications
• Custom applications: Internal tools and microservices
• Data formats: JSON, XML, CSV, EDI, and proprietary formats

Reference Architecture

Here's a production-grade enterprise automation architecture:

┌─────────────────────────────────────────────────────────────────┐
│                        External Users/Systems                    │
│              (Web, Mobile, APIs, Third-party Services)          │
└────────────────┬──────────────────┬─────────────────────────────┘
                 │                  │
┌────────────────▼──────────────────▼─────────────────────────────┐
│                    API Gateway Layer                             │
│  ┌──────────────┐  ┌──────────────┐  ┌───────────────────────┐ │
│  │     Kong     │  │   Auth0/     │  │   Rate Limiting &     │ │
│  │   Gateway    │  │   Okta       │  │   Throttling          │ │
│  └──────────────┘  └──────────────┘  └───────────────────────┘ │
└────────────────┬────────────────────────────────────────────────┘
                 │
┌────────────────▼────────────────────────────────────────────────┐
│                  Load Balancer (HAProxy/NGINX)                  │
└─────────┬────────────────┬────────────────┬─────────────────────┘
          │                │                │
┌─────────▼────────┐ ┌────▼─────────┐ ┌───▼──────────────────────┐
│  n8n Cluster     │ │  n8n Cluster │ │   n8n Cluster            │
│   (Primary)      │ │  (Secondary) │ │   (DR Site)              │
│                  │ │              │ │                          │
│ ┌──────────────┐ │ │ ┌──────────┐ │ │ ┌──────────────────────┐ │
│ │  Worker Pool │ │ │ │  Worker  │ │ │ │    Worker Pool       │ │
│ │   (10 nodes) │ │ │ │   Pool   │ │ │ │     (5 nodes)        │ │
│ └──────────────┘ │ │ └──────────┘ │ │ └──────────────────────┘ │
└─────────┬────────┘ └────┬─────────┘ └───┬──────────────────────┘
          │                │                │
┌─────────▼────────────────▼────────────────▼─────────────────────┐
│                    Data & State Layer                            │
│  ┌──────────────┐  ┌──────────────┐  ┌────────────────────────┐│
│  │  PostgreSQL  │  │    Redis     │  │   Message Queue        ││
│  │   (Primary)  │  │   Cluster    │  │   (RabbitMQ/Kafka)     ││
│  │  + Replicas  │  │              │  │                        ││
│  └──────────────┘  └──────────────┘  └────────────────────────┘│
└─────────────────────────────────────────────────────────────────┘
          │                │                │
┌─────────▼────────────────▼────────────────▼─────────────────────┐
│                  Integration & Service Layer                     │
│  ┌──────────────┐  ┌──────────────┐  ┌────────────────────────┐│
│  │  Enterprise  │  │   Logging    │  │     Monitoring         ││
│  │  Service Bus │  │  (ELK/Splunk)│  │  (Prometheus/Grafana)  ││
│  └──────────────┘  └──────────────┘  └────────────────────────┘│
│  ┌──────────────┐  ┌──────────────┐  ┌────────────────────────┐│
│  │   Secrets    │  │    Backup    │  │    Alert Manager       ││
│  │    Vault     │  │   Service    │  │   (PagerDuty/Opsgenie) ││
│  └──────────────┘  └──────────────┘  └────────────────────────┘│
└─────────────────────────────────────────────────────────────────┘

Layer-by-Layer Architecture

1. API Gateway Layer

The front door to your automation platform.

Kong API Gateway Configuration

# kong.yml
_format_version: '3.0'

services:
  - name: n8n-service
    url: http://n8n-cluster:5678
    routes:
      - name: n8n-routes
        paths:
          - /webhook
          - /rest
    plugins:
      - name: rate-limiting
        config:
          minute: 1000
          hour: 50000
          policy: redis
          redis:
            host: redis-cluster
            port: 6379
      - name: request-size-limiting
        config:
          allowed_payload_size: 10
      - name: correlation-id
        config:
          header_name: X-Correlation-ID
          generator: uuid
      - name: ip-restriction
        config:
          allow:
            - 10.0.0.0/8
            - 172.16.0.0/12

Authentication Layer

Integrate with enterprise identity providers:

// n8n HTTP Request node for Auth0 validation
const validateToken = async token => {
  const response = await fetch('https://your-tenant.auth0.com/userinfo', {
    headers: {
      Authorization: `Bearer ${token}`,
    },
  });

  if (!response.ok) {
    throw new Error('Invalid token');
  }

  const user = await response.json();

  // Check user permissions
  const permissions = user['https://your-app/permissions'] || [];

  return {
    user_id: user.sub,
    email: user.email,
    permissions: permissions,
    org_id: user['https://your-app/org_id'],
  };
};

2. High Availability n8n Cluster

Deploy n8n in a highly available configuration.

Docker Compose for n8n Cluster

# docker-compose.yml
version: '3.8'

services:
  n8n-main:
    image: n8nio/n8n:latest
    deploy:
      replicas: 3
      update_config:
        parallelism: 1
        delay: 10s
      restart_policy:
        condition: on-failure
        max_attempts: 3
    environment:
      - N8N_ENCRYPTION_KEY=${N8N_ENCRYPTION_KEY}
      - DB_TYPE=postgresdb
      - DB_POSTGRESDB_HOST=postgres-primary
      - DB_POSTGRESDB_PORT=5432
      - DB_POSTGRESDB_DATABASE=${POSTGRES_DB}
      - DB_POSTGRESDB_USER=${POSTGRES_USER}
      - DB_POSTGRESDB_PASSWORD=${POSTGRES_PASSWORD}
      - EXECUTIONS_MODE=queue
      - QUEUE_BULL_REDIS_HOST=redis-cluster
      - QUEUE_BULL_REDIS_PORT=6379
      - QUEUE_BULL_REDIS_DB=0
      - QUEUE_HEALTH_CHECK_ACTIVE=true
      - N8N_BASIC_AUTH_ACTIVE=false
      - N8N_DISABLE_UI=true
      - N8N_LOG_LEVEL=info
      - N8N_LOG_OUTPUT=console
      - WEBHOOK_URL=https://automation.company.com
      - GENERIC_TIMEZONE=America/New_York
      - N8N_METRICS=true
      - N8N_METRICS_PREFIX=n8n_
    healthcheck:
      test: ['CMD', 'wget', '--spider', '-q', 'http://localhost:5678/healthz']
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 60s
    networks:
      - automation-network
    volumes:
      - n8n-data:/home/node/.n8n

  n8n-worker:
    image: n8nio/n8n:latest
    deploy:
      replicas: 10
      resources:
        limits:
          cpus: '2'
          memory: 4G
        reservations:
          cpus: '1'
          memory: 2G
    environment:
      - N8N_ENCRYPTION_KEY=${N8N_ENCRYPTION_KEY}
      - DB_TYPE=postgresdb
      - DB_POSTGRESDB_HOST=postgres-primary
      - DB_POSTGRESDB_PORT=5432
      - DB_POSTGRESDB_DATABASE=${POSTGRES_DB}
      - DB_POSTGRESDB_USER=${POSTGRES_USER}
      - DB_POSTGRESDB_PASSWORD=${POSTGRES_PASSWORD}
      - EXECUTIONS_MODE=queue
      - QUEUE_BULL_REDIS_HOST=redis-cluster
      - QUEUE_BULL_REDIS_PORT=6379
      - QUEUE_WORKER_ID=${HOSTNAME}
      - N8N_LOG_LEVEL=info
    command: worker
    networks:
      - automation-network

  postgres-primary:
    image: postgres:15-alpine
    environment:
      - POSTGRES_DB=${POSTGRES_DB}
      - POSTGRES_USER=${POSTGRES_USER}
      - POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
      - POSTGRES_MAX_CONNECTIONS=200
      - POSTGRES_SHARED_BUFFERS=2GB
    volumes:
      - postgres-data:/var/lib/postgresql/data
      - ./postgres-config:/etc/postgresql
    command: postgres -c config_file=/etc/postgresql/postgresql.conf
    networks:
      - automation-network

  postgres-replica:
    image: postgres:15-alpine
    environment:
      - POSTGRES_PRIMARY_HOST=postgres-primary
      - POSTGRES_REPLICATION_MODE=slave
    networks:
      - automation-network

  redis-cluster:
    image: redis:7-alpine
    command: redis-server --appendonly yes --cluster-enabled yes
    volumes:
      - redis-data:/data
    networks:
      - automation-network

networks:
  automation-network:
    driver: overlay
    attachable: true

volumes:
  n8n-data:
  postgres-data:
  redis-data:

3. Database Layer Optimization

PostgreSQL Configuration for High Performance

# postgresql.conf
# Connection Settings
max_connections = 200
shared_buffers = 4GB
effective_cache_size = 12GB
maintenance_work_mem = 1GB
work_mem = 20MB

# WAL Settings for Replication
wal_level = replica
max_wal_senders = 10
max_replication_slots = 10
wal_keep_size = 1GB

# Query Performance
random_page_cost = 1.1
effective_io_concurrency = 200
max_worker_processes = 8
max_parallel_workers_per_gather = 4
max_parallel_workers = 8

# Checkpoint Settings
checkpoint_completion_target = 0.9
wal_buffers = 16MB
default_statistics_target = 100

# Logging
logging_collector = on
log_directory = 'pg_log'
log_filename = 'postgresql-%Y-%m-%d_%H%M%S.log'
log_rotation_age = 1d
log_rotation_size = 1GB
log_min_duration_statement = 1000
log_checkpoints = on
log_connections = on
log_disconnections = on
log_lock_waits = on

Database Indexing Strategy

-- Optimize n8n execution queries
CREATE INDEX CONCURRENTLY idx_executions_workflow_started
  ON execution_entity(workflow_id, started_at DESC);

CREATE INDEX CONCURRENTLY idx_executions_status
  ON execution_entity(status) WHERE status != 'success';

CREATE INDEX CONCURRENTLY idx_executions_waiting
  ON execution_entity(waiting_till)
  WHERE waiting_till IS NOT NULL;

-- Optimize webhook queries
CREATE INDEX CONCURRENTLY idx_webhook_path_method
  ON webhook_entity(webhook_path, method);

-- Partition large tables
CREATE TABLE execution_entity_2025_01 PARTITION OF execution_entity
  FOR VALUES FROM ('2025-01-01') TO ('2025-02-01');

CREATE TABLE execution_entity_2025_02 PARTITION OF execution_entity
  FOR VALUES FROM ('2025-02-01') TO ('2025-03-01');

-- Automated partition management
CREATE OR REPLACE FUNCTION create_monthly_partition()
RETURNS void AS $$
DECLARE
  partition_date DATE;
  partition_name TEXT;
  start_date TEXT;
  end_date TEXT;
BEGIN
  partition_date := DATE_TRUNC('month', CURRENT_DATE + INTERVAL '1 month');
  partition_name := 'execution_entity_' || TO_CHAR(partition_date, 'YYYY_MM');
  start_date := TO_CHAR(partition_date, 'YYYY-MM-DD');
  end_date := TO_CHAR(partition_date + INTERVAL '1 month', 'YYYY-MM-DD');

  EXECUTE format('CREATE TABLE IF NOT EXISTS %I PARTITION OF execution_entity FOR VALUES FROM (%L) TO (%L)',
    partition_name, start_date, end_date);
END;
$$ LANGUAGE plpgsql;

-- Schedule partition creation
SELECT cron.schedule('create-monthly-partitions', '0 0 1 * *', 'SELECT create_monthly_partition()');

4. Queue Management

For high-volume workflows, proper queue management is critical.

Redis Queue Configuration

// Custom queue settings for different workflow types
const queueConfigs = {
  critical: {
    priority: 1,
    attempts: 5,
    backoff: {
      type: 'exponential',
      delay: 2000,
    },
    timeout: 30000,
    removeOnComplete: 100,
    removeOnFail: 1000,
  },
  standard: {
    priority: 5,
    attempts: 3,
    backoff: {
      type: 'exponential',
      delay: 5000,
    },
    timeout: 300000,
    removeOnComplete: 50,
    removeOnFail: 500,
  },
  batch: {
    priority: 10,
    attempts: 2,
    backoff: {
      type: 'fixed',
      delay: 30000,
    },
    timeout: 3600000,
    removeOnComplete: 10,
    removeOnFail: 100,
  },
};

// Workflow classification
const getQueueConfig = workflowData => {
  const tags = workflowData.tags || [];

  if (tags.includes('critical') || tags.includes('realtime')) {
    return queueConfigs.critical;
  }

  if (tags.includes('batch') || tags.includes('bulk')) {
    return queueConfigs.batch;
  }

  return queueConfigs.standard;
};

Queue Monitoring

// Queue health monitoring workflow
const monitorQueues = async () => {
  const Bull = require('bull');
  const queue = new Bull('n8n-queue', {
    redis: {
      host: process.env.REDIS_HOST,
      port: process.env.REDIS_PORT,
    },
  });

  const metrics = {
    waiting: await queue.getWaitingCount(),
    active: await queue.getActiveCount(),
    delayed: await queue.getDelayedCount(),
    failed: await queue.getFailedCount(),
    completed: await queue.getCompletedCount(),
  };

  // Alert if queue is backing up
  if (metrics.waiting > 10000) {
    await sendAlert({
      severity: 'warning',
      message: `Queue backlog: ${metrics.waiting} jobs waiting`,
      metrics: metrics,
    });
  }

  // Alert if failure rate is high
  const failureRate = metrics.failed / (metrics.completed + metrics.failed);
  if (failureRate > 0.05) {
    // 5% failure threshold
    await sendAlert({
      severity: 'critical',
      message: `High failure rate: ${(failureRate * 100).toFixed(2)}%`,
      metrics: metrics,
    });
  }

  return metrics;
};

5. Security Framework

Secrets Management with HashiCorp Vault

// Vault integration for n8n credentials
const Vault = require('node-vault');

const vault = Vault({
  apiVersion: 'v1',
  endpoint: process.env.VAULT_ADDR,
  token: process.env.VAULT_TOKEN,
});

// Store credential
const storeCredential = async (credentialName, credentialData) => {
  await vault.write(`secret/data/n8n/credentials/${credentialName}`, {
    data: credentialData,
  });
};

// Retrieve credential
const getCredential = async credentialName => {
  const result = await vault.read(`secret/data/n8n/credentials/${credentialName}`);
  return result.data.data;
};

// Rotate credentials automatically
const rotateCredentials = async () => {
  const credentials = await vault.list('secret/metadata/n8n/credentials');

  for (const credName of credentials.data.keys) {
    const metadata = await vault.read(`secret/metadata/n8n/credentials/${credName}`);
    const created = new Date(metadata.data.created_time);
    const daysSinceCreation = (Date.now() - created) / (1000 * 60 * 60 * 24);

    if (daysSinceCreation > 90) {
      await sendAlert({
        severity: 'warning',
        message: `Credential ${credName} is ${daysSinceCreation.toFixed(0)} days old and should be rotated`,
      });
    }
  }
};

Row-Level Security

-- Enable row-level security
ALTER TABLE workflow_entity ENABLE ROW LEVEL SECURITY;
ALTER TABLE execution_entity ENABLE ROW LEVEL SECURITY;

-- Create policies for multi-tenant isolation
CREATE POLICY tenant_isolation ON workflow_entity
  USING (organization_id = current_setting('app.current_organization')::int);

CREATE POLICY tenant_isolation ON execution_entity
  USING (organization_id = current_setting('app.current_organization')::int);

-- Function to set tenant context
CREATE OR REPLACE FUNCTION set_tenant_context(org_id INT)
RETURNS void AS $$
BEGIN
  PERFORM set_config('app.current_organization', org_id::text, false);
END;
$$ LANGUAGE plpgsql SECURITY DEFINER;

6. Observability and Monitoring

Prometheus Metrics Export

// Custom metrics for n8n workflows
const prometheus = require('prom-client');

// Register metrics
const workflowExecutions = new prometheus.Counter({
  name: 'n8n_workflow_executions_total',
  help: 'Total number of workflow executions',
  labelNames: ['workflow_id', 'workflow_name', 'status'],
});

const workflowDuration = new prometheus.Histogram({
  name: 'n8n_workflow_duration_seconds',
  help: 'Workflow execution duration in seconds',
  labelNames: ['workflow_id', 'workflow_name'],
  buckets: [0.1, 0.5, 1, 5, 10, 30, 60, 300, 600],
});

const nodeExecutions = new prometheus.Counter({
  name: 'n8n_node_executions_total',
  help: 'Total number of node executions',
  labelNames: ['node_type', 'status'],
});

const queueSize = new prometheus.Gauge({
  name: 'n8n_queue_size',
  help: 'Current size of the execution queue',
  labelNames: ['queue_type'],
});

// Record metrics in workflow
const recordWorkflowMetrics = execution => {
  workflowExecutions.inc({
    workflow_id: execution.workflowId,
    workflow_name: execution.workflowName,
    status: execution.status,
  });

  const duration = (execution.finishedAt - execution.startedAt) / 1000;
  workflowDuration.observe(
    {
      workflow_id: execution.workflowId,
      workflow_name: execution.workflowName,
    },
    duration
  );

  // Record node metrics
  execution.nodes?.forEach(node => {
    nodeExecutions.inc({
      node_type: node.type,
      status: node.status,
    });
  });
};

Grafana Dashboard Configuration

{
  "dashboard": {
    "title": "n8n Enterprise Monitoring",
    "panels": [
      {
        "title": "Workflow Execution Rate",
        "targets": [
          {
            "expr": "rate(n8n_workflow_executions_total[5m])",
            "legendFormat": "{{workflow_name}}"
          }
        ]
      },
      {
        "title": "Workflow Success Rate",
        "targets": [
          {
            "expr": "sum(rate(n8n_workflow_executions_total{status='success'}[5m])) / sum(rate(n8n_workflow_executions_total[5m]))",
            "legendFormat": "Success Rate"
          }
        ]
      },
      {
        "title": "P95 Execution Duration",
        "targets": [
          {
            "expr": "histogram_quantile(0.95, rate(n8n_workflow_duration_seconds_bucket[5m]))",
            "legendFormat": "{{workflow_name}}"
          }
        ]
      },
      {
        "title": "Queue Depth",
        "targets": [
          {
            "expr": "n8n_queue_size",
            "legendFormat": "{{queue_type}}"
          }
        ]
      }
    ]
  }
}

7. Disaster Recovery

Automated Backup Strategy

#!/bin/bash
# backup-n8n.sh

# Configuration
BACKUP_DIR="/backups/n8n"
RETENTION_DAYS=30
S3_BUCKET="s3://company-backups/n8n"
TIMESTAMP=$(date +%Y%m%d_%H%M%S)

# Create backup directory
mkdir -p "$BACKUP_DIR/$TIMESTAMP"

# Backup PostgreSQL database
echo "Backing up PostgreSQL..."
pg_dump -h $POSTGRES_HOST -U $POSTGRES_USER -d $POSTGRES_DB \
  --format=custom \
  --file="$BACKUP_DIR/$TIMESTAMP/n8n-database.dump"

# Backup n8n data directory
echo "Backing up n8n data..."
tar -czf "$BACKUP_DIR/$TIMESTAMP/n8n-data.tar.gz" /home/node/.n8n

# Backup configuration
echo "Backing up configuration..."
kubectl get configmaps -n n8n -o yaml > "$BACKUP_DIR/$TIMESTAMP/configmaps.yaml"
kubectl get secrets -n n8n -o yaml > "$BACKUP_DIR/$TIMESTAMP/secrets.yaml"

# Upload to S3
echo "Uploading to S3..."
aws s3 sync "$BACKUP_DIR/$TIMESTAMP" "$S3_BUCKET/$TIMESTAMP" \
  --storage-class STANDARD_IA

# Cleanup old backups
echo "Cleaning up old backups..."
find "$BACKUP_DIR" -type d -mtime +$RETENTION_DAYS -exec rm -rf {} +

# Verify backup
echo "Verifying backup..."
aws s3 ls "$S3_BUCKET/$TIMESTAMP/" | grep -q "n8n-database.dump"
if [ $? -eq 0 ]; then
  echo "Backup completed successfully"
else
  echo "Backup verification failed!" >&2
  exit 1
fi

Recovery Procedures

#!/bin/bash
# restore-n8n.sh

BACKUP_TIMESTAMP=$1
S3_BUCKET="s3://company-backups/n8n"
RESTORE_DIR="/tmp/n8n-restore"

if [ -z "$BACKUP_TIMESTAMP" ]; then
  echo "Usage: $0 <backup_timestamp>"
  exit 1
fi

# Download from S3
echo "Downloading backup from S3..."
mkdir -p "$RESTORE_DIR"
aws s3 sync "$S3_BUCKET/$BACKUP_TIMESTAMP" "$RESTORE_DIR"

# Stop n8n services
echo "Stopping n8n services..."
kubectl scale deployment n8n-main --replicas=0 -n n8n
kubectl scale deployment n8n-worker --replicas=0 -n n8n

# Restore database
echo "Restoring database..."
pg_restore -h $POSTGRES_HOST -U $POSTGRES_USER -d $POSTGRES_DB \
  --clean --if-exists \
  "$RESTORE_DIR/n8n-database.dump"

# Restore n8n data
echo "Restoring n8n data..."
tar -xzf "$RESTORE_DIR/n8n-data.tar.gz" -C /

# Restore configuration
echo "Restoring configuration..."
kubectl apply -f "$RESTORE_DIR/configmaps.yaml"
kubectl apply -f "$RESTORE_DIR/secrets.yaml"

# Restart n8n services
echo "Starting n8n services..."
kubectl scale deployment n8n-main --replicas=3 -n n8n
kubectl scale deployment n8n-worker --replicas=10 -n n8n

# Verify restoration
echo "Verifying restoration..."
sleep 30
kubectl get pods -n n8n | grep Running
if [ $? -eq 0 ]; then
  echo "Restoration completed successfully"
else
  echo "Restoration verification failed!" >&2
  exit 1
fi

Governance and Operations

Change Management Process

# workflow-approval.yml
# GitOps-based workflow approval process

apiVersion: v1
kind: Workflow
metadata:
  name: workflow-change-approval
spec:
  steps:
    - name: validate-workflow
      script: |
        # Validate workflow JSON schema
        # Check for security issues
        # Verify credential references
        # Test in staging environment

    - name: peer-review
      approval:
        required_reviewers: 2
        allowed_reviewers:
          - engineering-team
        timeout: 48h

    - name: security-review
      approval:
        required_reviewers: 1
        allowed_reviewers:
          - security-team
        timeout: 24h
        condition: workflow.uses_sensitive_data

    - name: deploy-to-staging
      script: |
        kubectl apply -f workflow.yaml -n n8n-staging

    - name: integration-tests
      script: |
        npm run test:integration

    - name: production-approval
      approval:
        required_reviewers: 1
        allowed_reviewers:
          - platform-owners
        timeout: 24h

    - name: deploy-to-production
      script: |
        kubectl apply -f workflow.yaml -n n8n-production

Cost Management

// Cost tracking and optimization
const trackWorkflowCosts = async execution => {
  const costs = {
    compute_time: calculateComputeCost(execution.duration),
    api_calls: calculateAPICallCosts(execution.nodes),
    data_transfer: calculateDataTransferCost(execution.dataSize),
    storage: calculateStorageCost(execution.dataRetention),
  };

  const totalCost = Object.values(costs).reduce((a, b) => a + b, 0);

  await supabase.from('workflow_costs').insert({
    workflow_id: execution.workflowId,
    execution_id: execution.id,
    costs: costs,
    total_cost: totalCost,
    timestamp: new Date(),
  });

  // Alert on high-cost workflows
  if (totalCost > 10) {
    // $10 threshold
    await sendAlert({
      severity: 'info',
      message: `High-cost workflow execution: ${execution.workflowName} cost $${totalCost.toFixed(2)}`,
      workflow_id: execution.workflowId,
    });
  }
};

// Monthly cost reporting
const generateCostReport = async month => {
  const report = await supabase
    .from('workflow_costs')
    .select('workflow_id, workflow_name, sum(total_cost) as monthly_cost')
    .gte('timestamp', `${month}-01`)
    .lt('timestamp', `${month + 1}-01`)
    .groupBy('workflow_id', 'workflow_name')
    .order('monthly_cost', { ascending: false });

  return report;
};

Performance Optimization Strategies

Workflow Design Patterns

1. Micro-workflows: Break large workflows into smaller, reusable components
2. Lazy loading: Only fetch data when needed
3. Batching: Process multiple items together
4. Caching: Store frequently accessed data
5. Async processing: Use queues for long-running tasks

Database Query Optimization

// Bad: N+1 query problem
for (const user of users) {
  const orders = await getOrders(user.id);
  // Process orders
}

// Good: Batch query
const userIds = users.map(u => u.id);
const allOrders = await getOrdersBatch(userIds);
const ordersByUser = groupBy(allOrders, 'user_id');

for (const user of users) {
  const orders = ordersByUser[user.id] || [];
  // Process orders
}

Caching Strategy

// Multi-level caching
const cache = {
  // L1: In-memory cache (fast, small)
  memory: new Map(),

  // L2: Redis cache (fast, larger)
  redis: redisClient,

  // L3: Database (slower, permanent)
  database: postgresClient,
};

const getCachedData = async key => {
  // Check L1
  if (cache.memory.has(key)) {
    return cache.memory.get(key);
  }

  // Check L2
  const redisValue = await cache.redis.get(key);
  if (redisValue) {
    cache.memory.set(key, redisValue);
    return redisValue;
  }

  // Check L3
  const dbValue = await cache.database.query('SELECT * FROM cache WHERE key = $1', [key]);
  if (dbValue) {
    await cache.redis.set(key, dbValue, 'EX', 3600);
    cache.memory.set(key, dbValue);
    return dbValue;
  }

  return null;
};

Compliance and Audit

Audit Logging

-- Comprehensive audit trail
CREATE TABLE audit_log (
  id SERIAL PRIMARY KEY,
  timestamp TIMESTAMP DEFAULT NOW(),
  user_id VARCHAR(255),
  action VARCHAR(100),
  resource_type VARCHAR(100),
  resource_id VARCHAR(255),
  old_value JSONB,
  new_value JSONB,
  ip_address INET,
  user_agent TEXT,
  session_id VARCHAR(255)
);

-- Trigger for workflow changes
CREATE OR REPLACE FUNCTION audit_workflow_changes()
RETURNS TRIGGER AS $$
BEGIN
  INSERT INTO audit_log (user_id, action, resource_type, resource_id, old_value, new_value)
  VALUES (
    current_setting('app.current_user'),
    TG_OP,
    'workflow',
    NEW.id,
    row_to_json(OLD),
    row_to_json(NEW)
  );
  RETURN NEW;
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER workflow_audit
  AFTER INSERT OR UPDATE OR DELETE ON workflow_entity
  FOR EACH ROW EXECUTE FUNCTION audit_workflow_changes();

Migration and Integration Patterns

Legacy System Integration

// Adapter pattern for legacy SOAP services
class LegacySystemAdapter {
  constructor(wsdlUrl) {
    this.client = soap.createClient(wsdlUrl);
  }

  async callLegacyService(operation, params) {
    try {
      const result = await this.client[operation](params);
      return this.transformResponse(result);
    } catch (error) {
      // Handle common legacy system errors
      if (error.code === 'TIMEOUT') {
        await this.logError('Legacy system timeout', { operation, params });
        throw new Error('Service temporarily unavailable');
      }
      throw error;
    }
  }

  transformResponse(legacyResponse) {
    // Transform XML/SOAP response to modern JSON
    return {
      success: legacyResponse.status === '0',
      data: this.parseData(legacyResponse.data),
      metadata: {
        timestamp: new Date().toISOString(),
        source: 'legacy_system',
      },
    };
  }
}

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